Prototype learning. Learning representative features of a class or group of samples has been a well explored problem in pattern recognition. Originally calculated with hand-crafted features[1]}, recent approaches use convolutional neural networks for feature extraction. This method has seen success in a variety of tasks, including classification [2]}, zero-shot recognition [3]}, and domain adaptive 2D segmentation [4]}. We argue that when training a network with noisy pseudo-labels, clustering and outlier removal are insufficient in obtaining a true representative class prototype. We thus propose a transformer-based approach to generate attentive prototypes.
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